Feature Selection Method Based on Improved Monarch Butterfly Optimization Algorithm
SUN Lin1,2, ZHAO Jing1,2, XU Jiucheng1,2, XUE Zhan'ao1,2
1. College of Computer and Information Engineering,Henan Nor-mal University,Xinxiang 453007; 2. Engineering Laboratory of Intelligence Business and Internet of Things Technologies,Henan Normal University,Xinxiang 453007
Abstract Aiming at the weak global search ability and the reduction of population diversity during migration of monarch butterfly optimization(MBO) algorithm,a differential adaptive MBO algorithm based on Cauchy mutation and its feature selection method are proposed.Firstly,the MBO migration operator is replaced by the mutation operation in the differential evolution algorithm to improve the global search ability.Then,MBO adjustment operator is combined with the adaptive adjustment strategy to change the single adjustment mode.Finally,Cauchy mutation is conducted in each updated population to increase population diversity.To verify the performance of the improved MBO algorithm and its feature selection method,experiments on benchmark functions and UCI datasets are conducted,and the results show that the proposed algorithms produce better performance than other algorithms.
Corresponding Authors:
SUN Lin,Ph.D.,associate professor.His research interests include granular computing,big data mining and intelligent information processing.
About author:: ZHAO Jing,master student.Her research interests include intelligent information processing and data mining.XU Jiucheng,Ph.D.,professor.His research interests include granular computing,big data mining and intelligent information processing.XUE Zhanao,Ph.D.,professor.His research interests include basic theory of artificial intelligence and rough sets theory.
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2020, Vol. 33 
